Machine Learning Tutorial for the UKP lab. June 10 2011 ... ▫“The goal of
machine learning is to build computer systems that can adapt and learn from their
.
Scaling Up Machine Learning. Parallel and Distributed Approaches. Ron
Bekkerman, LinkedIn. Misha Bilenko, MSR. John Langford, Y!R http://hunch.net/~
...
... state of the global air traffic network ... Then build good tools and take scientific approach to exploring ..... Ta
New age of big data. ⢠The world has gone mobile. â 5 billion cellphones produce daily data. ⢠Social networks hav
Full stack product team calls backend team APIs ... Spam content and de-duping ..... Facebook for risk: Streamline inves
the field known as data mining, machine learning algorithms are being used rou- ... has used its learned strategies to d
people's response time improves with practice according to a power law. ... settings in which the learner may pose vario
target function V and again use the notation V : B + 8 to denote that V maps ..... the task of classifying text document
data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific
Motivation. Examples. Theoretical Problems. Results. Perspectives. Learning. Machine Learning: develop algorithms to aut
Journal of Machine Learning Research 10 (2009) 931-934 ... Java-ML is a
collection of machine learning and data mining algorithms, which aims to be a
readily.
models in three tasks considered later in this paper (for more detail, see the supplementary material1). Once pos- terior probabilities are available, the ...
Machine learning deals with the design of computer programs and .... Unsupervised Learning. ⢠Supervised learning (classification, regression). â Input patterns ...
Jun 10, 2011 - f f. â«Example: set of queries and a set of top retrieved documents. (characterized via tf, idf, tf*idf,
and Frank, 2005) and Yale/RapidMiner (Mierswa et al., 2006). .... samples as well as the PDF versions of the tutorials are also included in the Java-ML ...
Sebastian Raschka and Vahid Mirjalili s unique insight and expertise introduce ... in data with clusteringDelve deeper i
Training powerful but computationally-expensive deep models on: â Terabyte or petabyte-sized training datasets. Plus t
Nov 28, 2016 - Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, .... [87] Alejandro Perdomo-Ortiz, Bryan O'Gorman, Joseph. Fluegemann, Rupak Biswas ...
Machine Learning with MALLET h%p://mallet.cs.umass.edu. David Mimno.
Informa4on Extrac4on and Synthesis. Laboratory, Department of CS. UMass ...
This is a tutorial by dummies and for everyone. Stroppa and Chrupala () ....
Machine Learning gives sound and theoretically-rooted principles for:
Automatically ...
Dec 2, 2013 ... CBBP, Theoretical Physics. Machine Learning. Machine Learning is the study of
computer algorithms that learn and improve through ...
Nov 9, 2015 - A possible method used in this paper for defining such ..... grant PRELUDIUM 2013/09/N/NZ2/01917 financed by the Polish National Science ...
May 30, 2014 - K-means (Section 10.5), and a different flavor of quantum support vector machines (Section 12.3). Regression based on quantum process ...
Future Work. ⢠Develop larger datasets. ⢠Explore different feature performance. ⢠Hydra: an OST parser using evol
Meaning-Based Machine Learning Dr. Courtney Falk Infinite Machines
Who Am I? • Day job • Senior research scientist at Optiv • Threat intelligence reporting • Some work on ontologies for information security applications
• Built for natural language processing • No logical formalism a la Web Ontology Language (OWL) • Frame-based inheritance • Output structure known as a Text Meaning Representation (TMR)
Fact DB
Onomasticon
Abstract
Concrete
Resource Examples Concept
Word Sense (German) “fressen”-VERB1 SYN-STRUC subject
(EAT
(IS-A (VALUE (BIOLOGICAL-EVENT))) var 1 cat noun fressen verb
(DEFINITION (VALUE (“Consumption of nutrition.”))) (AGENT (SEM (ANIMATE-OBJECT))) (THEME (SEM (FOOD))) )
Semantics from Machine Learning • Latent semantics analysis/indexing (LSA/LSI) • Singular value decomposition (SVD) dimensionality reduction • Concepts are groups of spatially proximate words
• Latent Dirichlet allocation (LDA) • Hierarchical topic model
• Word2vec • Neural networks • Vector space model (VSM)
• But are the structured learning meaningful to humans?
Meaning-Based Machine Learning • Start with meaningful data • Manually defined by human acquirers
• Use ML to find meaningful patterns • MBML for Information Assurance (2016) • Application to information security problems: phishing detection, stylometry, et.al.
Knowledge Modeling of Phishing Emails • Manually generated TMRs
• 28 phishing emails from the Anti-Phishing Working Group (APWG) • 28 known good emails from my inboxes
• Train binary classifiers on TMR structures
• Three algorithms: Naïve-Bayes, J48 (C4.5), and SVM • Compare learning on decomposed TMRs to unigram language models • Used K-fold cross validation to avoid overfitting
• Positives
• Performed better than unigram language models • Confidence intervals were smaller for semantic results
• Negatives
• Small sample size (not necessarily generalizable) • Didn’t record lexeme -> concept mappings
Feature Design “Johnny gave Jane the cake” (GIVE-37 {GIVE:AGENT:VALUE:HUMAN, (AGENT (VALUE (HUMAN-4))) (THEME (VALUE (BAKED-CAKE-78))) GIVE:THEME:VALUE:BAKED-CAKE, GIVE:BENEFICIARY:VALUE:HUMAN} (BENEFICIARY (VALUE (HUMAN-91))) ) Generates features
Experimental Results
Generated Decision Trees
Future Work • Develop larger datasets • Explore different feature performance • Hydra: an OST parser using evolutionary algorithms • Bootstrapping from LSA/LDA into lexemes and word senses • New applications outside of phishing detection
References • Onyshkevich, B. and Nirenburg, S. (1995) A lexicon for knowledge-based MT. Machine Translation, 10(1), pp. 5-57. • Nirenburg, S. and Raskin, V. (2004) Ontological semantics. Cambridge, MA: MIT Press. • Taylor, J. and Raskin, V. (2010) Fuzzy ontology for natural language. 2010 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 1-6. • Falk C. and Stuart L. (2016) Meaning-based machine learning. Journal of Innovation in Digital Ecosystems, 3(2), pp. 141-147. • Falk C. (2016) Knowledge modeling of phishing emails (Doctoral dissertation). Retrieved from ProQuest. (10170565)
